Lockman SWIRE master catalogue

Checks and diagnostics

In [1]:
from herschelhelp_internal import git_version
print("This notebook was run with herschelhelp_internal version: \n{}".format(git_version()))
This notebook was run with herschelhelp_internal version: 
255270d (Fri Nov 24 10:35:51 2017 +0000)
In [2]:
%matplotlib inline
#%config InlineBackend.figure_format = 'svg'

import matplotlib.pyplot as plt
plt.rc('figure', figsize=(10, 6))
plt.style.use('ggplot')

import locale
locale.setlocale(locale.LC_ALL, 'en_GB')

import os
import time
import itertools

from astropy.coordinates import SkyCoord
from astropy.table import Table
from astropy import units as u
from astropy import visualization as vis
import numpy as np
from matplotlib_venn import venn3

from herschelhelp_internal.masterlist import nb_compare_mags, nb_ccplots, nb_histograms, find_last_ml_suffix
In [3]:
OUT_DIR = os.environ.get('OUT_DIR', "./data")
SUFFIX = find_last_ml_suffix()
#SUFFIX = "20170710"

master_catalogue_filename = "master_catalogue_lockman-swire_{}.fits".format(SUFFIX)
master_catalogue = Table.read("{}/{}".format(OUT_DIR, master_catalogue_filename))

print("Diagnostics done using: {}".format(master_catalogue_filename))
Diagnostics done using: master_catalogue_lockman-swire_20171128.fits

I - Summary of wavelength domains

In [4]:
flag_obs = master_catalogue['flag_optnir_obs']
flag_det = master_catalogue['flag_optnir_det']
In [5]:
venn3(
    [
        np.sum(flag_obs == 4),
        np.sum(flag_obs == 2),
        np.sum(flag_obs == 6),
        np.sum(flag_obs == 1),
        np.sum(flag_obs == 5),
        np.sum(flag_obs == 3),
        np.sum(flag_obs == 7)
    ],
    set_labels=('Optical', 'near-IR', 'mid-IR'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/len(flag_obs)))
)
plt.title("Wavelength domain observations");
In [6]:
venn3(
    [
        np.sum(flag_det[flag_obs == 7] == 4),
        np.sum(flag_det[flag_obs == 7] == 2),
        np.sum(flag_det[flag_obs == 7] == 6),
        np.sum(flag_det[flag_obs == 7] == 1),
        np.sum(flag_det[flag_obs == 7] == 5),
        np.sum(flag_det[flag_obs == 7] == 3),
        np.sum(flag_det[flag_obs == 7] == 7)
    ],
    set_labels=('mid-IR', 'near-IR', 'Optical'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/np.sum(flag_det != 0)))
)
plt.title("Detection of the {} sources detected\n in any wavelength domains "
          "(among {} sources)".format(
              locale.format('%d', np.sum(flag_det != 0), grouping=True),
              locale.format('%d', len(flag_det), grouping=True)));

II - Comparing magnitudes in similar filters

The master list if composed of several catalogues containing magnitudes in similar filters on different instruments. We are comparing the magnitudes in these corresponding filters.

In [7]:
u_bands = ["WFC u",          "CFHT Megacam u"]
g_bands = ["WFC g", "RCS g", "CFHT Megacam g",  "GPC1 g"]
r_bands = ["WFC r", "RCS r", "CFHT Megacam r",  "GPC1 r"]
i_bands = ["WFC i", "RCS i",                    "GPC1 i"]
z_bands = ["WFC z", "RCS z", "CFHT Megacam z",  "GPC1 z"]
y_bands = [         "RCS y",                    "GPC1 y"]

II.a - Comparing depths

We compare the histograms of the total aperture magnitudes of similar bands.

In [8]:
for bands in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    colnames = ["m_{}".format(band.replace(" ", "_").lower()) for band in bands]
    nb_histograms(master_catalogue, colnames, bands)

II.b - Comparing magnitudes

We compare one to one each magnitude in similar bands.

In [9]:
for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    for band1, band2 in itertools.combinations(band_of_a_kind, 2):
        
        basecol1, basecol2 = band1.replace(" ", "_").lower(), band2.replace(" ", "_").lower()
        
        col1, col2 = "m_ap_{}".format(basecol1), "m_ap_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (aperture)".format(band1), "{} (aperture)".format(band2)))
        
        col1, col2 = "m_{}".format(basecol1), "m_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (total)".format(band1), "{} (total)".format(band2)))
CFHT Megacam u (aperture) - WFC u (aperture):
- Median: -0.06
- Median Absolute Deviation: 0.25
- 1% percentile: -0.9491949844360351
- 99% percentile: 3.2663552665710673
CFHT Megacam u (total) - WFC u (total):
- Median: -0.27
- Median Absolute Deviation: 0.21
- 1% percentile: -1.6218692207336425
- 99% percentile: 2.0539311504363935
No sources have both WFC g (aperture) and RCS g (aperture) values.
RCS g (total) - WFC g (total):
- Median: -0.08
- Median Absolute Deviation: 0.18
- 1% percentile: -0.9441648864746093
- 99% percentile: 1.156666412353516
CFHT Megacam g (aperture) - WFC g (aperture):
- Median: 0.11
- Median Absolute Deviation: 0.14
- 1% percentile: -0.5078629302978516
- 99% percentile: 0.8724484062194824
CFHT Megacam g (total) - WFC g (total):
- Median: -0.04
- Median Absolute Deviation: 0.15
- 1% percentile: -0.8757493972778321
- 99% percentile: 1.4329431533813466
GPC1 g (aperture) - WFC g (aperture):
- Median: -0.14
- Median Absolute Deviation: 0.27
- 1% percentile: -2.033579864501953
- 99% percentile: 2.2427005767822266
GPC1 g (total) - WFC g (total):
- Median: -0.03
- Median Absolute Deviation: 0.24
- 1% percentile: -2.5378850173950194
- 99% percentile: 1.7712992477417002
No sources have both RCS g (aperture) and CFHT Megacam g (aperture) values.
CFHT Megacam g (total) - RCS g (total):
- Median: 0.05
- Median Absolute Deviation: 0.18
- 1% percentile: -1.0496853256225587
- 99% percentile: 1.2588200759887684
No sources have both RCS g (aperture) and GPC1 g (aperture) values.
GPC1 g (total) - RCS g (total):
- Median: 0.04
- Median Absolute Deviation: 0.31
- 1% percentile: -3.0529003143310547
- 99% percentile: 1.9456966972351046
GPC1 g (aperture) - CFHT Megacam g (aperture):
- Median: -0.33
- Median Absolute Deviation: 0.38
- 1% percentile: -2.9429248237609866
- 99% percentile: 2.1852089309692397
GPC1 g (total) - CFHT Megacam g (total):
- Median: 0.01
- Median Absolute Deviation: 0.27
- 1% percentile: -3.230147476196289
- 99% percentile: 1.8613366317748992
No sources have both WFC r (aperture) and RCS r (aperture) values.
RCS r (total) - WFC r (total):
- Median: -0.13
- Median Absolute Deviation: 0.15
- 1% percentile: -1.0184492111206054
- 99% percentile: 1.12237449645996
CFHT Megacam r (aperture) - WFC r (aperture):
- Median: 0.13
- Median Absolute Deviation: 0.15
- 1% percentile: -0.545387954711914
- 99% percentile: 1.148755531311045
CFHT Megacam r (total) - WFC r (total):
- Median: -0.08
- Median Absolute Deviation: 0.14
- 1% percentile: -0.9593395805358886
- 99% percentile: 1.6030628204345634
GPC1 r (aperture) - WFC r (aperture):
- Median: -0.10
- Median Absolute Deviation: 0.15
- 1% percentile: -1.2963315582275392
- 99% percentile: 1.1390761566162118
GPC1 r (total) - WFC r (total):
- Median: -0.01
- Median Absolute Deviation: 0.14
- 1% percentile: -1.7165836334228515
- 99% percentile: 0.9766998291015625
No sources have both RCS r (aperture) and CFHT Megacam r (aperture) values.
CFHT Megacam r (total) - RCS r (total):
- Median: 0.07
- Median Absolute Deviation: 0.17
- 1% percentile: -1.103610725402832
- 99% percentile: 1.3413280487060542
No sources have both RCS r (aperture) and GPC1 r (aperture) values.
GPC1 r (total) - RCS r (total):
- Median: 0.13
- Median Absolute Deviation: 0.14
- 1% percentile: -1.851234474182129
- 99% percentile: 1.0487995147705078
GPC1 r (aperture) - CFHT Megacam r (aperture):
- Median: -0.26
- Median Absolute Deviation: 0.25
- 1% percentile: -2.3193286895751952
- 99% percentile: 1.125625000000003
GPC1 r (total) - CFHT Megacam r (total):
- Median: 0.07
- Median Absolute Deviation: 0.14
- 1% percentile: -2.370811748504639
- 99% percentile: 1.0244120407104438
No sources have both WFC i (aperture) and RCS i (aperture) values.
RCS i (total) - WFC i (total):
- Median: -0.08
- Median Absolute Deviation: 0.20
- 1% percentile: -0.9779530334472656
- 99% percentile: 1.4092513275146472
GPC1 i (aperture) - WFC i (aperture):
- Median: -0.03
- Median Absolute Deviation: 0.14
- 1% percentile: -1.025598430633545
- 99% percentile: 0.9575976371765118
GPC1 i (total) - WFC i (total):
- Median: 0.06
- Median Absolute Deviation: 0.15
- 1% percentile: -1.102666082382202
- 99% percentile: 0.9996103668212877
No sources have both RCS i (aperture) and GPC1 i (aperture) values.
GPC1 i (total) - RCS i (total):
- Median: 0.17
- Median Absolute Deviation: 0.12
- 1% percentile: -1.615425796508789
- 99% percentile: 0.8306423950195325
No sources have both WFC z (aperture) and RCS z (aperture) values.
RCS z (total) - WFC z (total):
- Median: -0.05
- Median Absolute Deviation: 0.24
- 1% percentile: -0.999578857421875
- 99% percentile: 1.782779693603516
CFHT Megacam z (aperture) - WFC z (aperture):
- Median: 0.14
- Median Absolute Deviation: 0.17
- 1% percentile: -0.6397380828857422
- 99% percentile: 3.043675384521484
CFHT Megacam z (total) - WFC z (total):
- Median: -0.04
- Median Absolute Deviation: 0.22
- 1% percentile: -0.9713739395141602
- 99% percentile: 2.110036201477051
GPC1 z (aperture) - WFC z (aperture):
- Median: 0.01
- Median Absolute Deviation: 0.15
- 1% percentile: -0.9230419921874999
- 99% percentile: 1.1071548843383785
GPC1 z (total) - WFC z (total):
- Median: 0.09
- Median Absolute Deviation: 0.19
- 1% percentile: -0.9829817771911621
- 99% percentile: 1.4533742713928244
No sources have both RCS z (aperture) and CFHT Megacam z (aperture) values.
CFHT Megacam z (total) - RCS z (total):
- Median: 0.05
- Median Absolute Deviation: 0.24
- 1% percentile: -1.255912399291992
- 99% percentile: 2.259424972534189
No sources have both RCS z (aperture) and GPC1 z (aperture) values.
GPC1 z (total) - RCS z (total):
- Median: 0.15
- Median Absolute Deviation: 0.18
- 1% percentile: -1.9935058212280274
- 99% percentile: 1.3433990478515625
GPC1 z (aperture) - CFHT Megacam z (aperture):
- Median: -0.22
- Median Absolute Deviation: 0.26
- 1% percentile: -2.777841310501098
- 99% percentile: 1.4052535629272451
GPC1 z (total) - CFHT Megacam z (total):
- Median: 0.12
- Median Absolute Deviation: 0.18
- 1% percentile: -2.3167495727539062
- 99% percentile: 1.362018585205071
No sources have both RCS y (aperture) and GPC1 y (aperture) values.
GPC1 y (total) - RCS y (total):
- Median: -0.57
- Median Absolute Deviation: 0.41
- 1% percentile: -3.220444412231445
- 99% percentile: 1.1729692840576167

III - Comparing magnitudes to reference bands

Cross-match the master list to SDSS and 2MASS to compare its magnitudes to SDSS and 2MASS ones.

In [10]:
master_catalogue_coords = SkyCoord(master_catalogue['ra'], master_catalogue['dec'])

III.a - Comparing u, g, r, i, and z bands to SDSS

The catalogue is cross-matched to SDSS-DR13 withing 0.2 arcsecond.

We compare the u, g, r, i, and z magnitudes to those from SDSS using fiberMag for the aperture magnitude and petroMag for the total magnitude.

In [11]:
sdss = Table.read("../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_Lockman-SWIRE.fits")
sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)

idx, d2d, _ = sdss_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

sdss = sdss[mask]
ml_sdss_idx = idx[mask]
In [12]:
for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands]:
    for band in band_of_a_kind:
        
        sdss_mag_ap = sdss["fiberMag_{}".format(band[-1])]
        master_cat_mag_ap = master_catalogue["m_ap_{}".format(band.replace(" ", "_").lower())][ml_sdss_idx]
    
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (fiberMag)".format(band[-1]), "{} (aperture)".format(band)))
    
        sdss_mag_tot = sdss["petroMag_{}".format(band[-1])]
        master_cat_mag_tot = master_catalogue["m_ap_{}".format(band.replace(" ", "_").lower())][ml_sdss_idx]
        
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (petroMag)".format(band[-1]), "{} (total)".format(band)))
WFC u (aperture) - SDSS u (fiberMag):
- Median: -0.31
- Median Absolute Deviation: 0.18
- 1% percentile: -1.5710824012756348
- 99% percentile: 0.7945964813232422
WFC u (total) - SDSS u (petroMag):
- Median: -0.31
- Median Absolute Deviation: 0.18
- 1% percentile: -1.5710824012756348
- 99% percentile: 0.7945964813232422
CFHT Megacam u (aperture) - SDSS u (fiberMag):
- Median: 0.01
- Median Absolute Deviation: 0.52
- 1% percentile: -1.7239515686035158
- 99% percentile: 3.037174682617188
CFHT Megacam u (total) - SDSS u (petroMag):
- Median: 0.01
- Median Absolute Deviation: 0.52
- 1% percentile: -1.7239515686035158
- 99% percentile: 3.037174682617188
WFC g (aperture) - SDSS g (fiberMag):
- Median: -0.45
- Median Absolute Deviation: 0.12
- 1% percentile: -1.153176498413086
- 99% percentile: 0.4261890411376952
WFC g (total) - SDSS g (petroMag):
- Median: -0.45
- Median Absolute Deviation: 0.12
- 1% percentile: -1.153176498413086
- 99% percentile: 0.4261890411376952
No sources have both SDSS g (fiberMag) and RCS g (aperture) values.
No sources have both SDSS g (petroMag) and RCS g (total) values.
CFHT Megacam g (aperture) - SDSS g (fiberMag):
- Median: -0.29
- Median Absolute Deviation: 0.18
- 1% percentile: -1.0998111724853517
- 99% percentile: 1.262142181396477
CFHT Megacam g (total) - SDSS g (petroMag):
- Median: -0.29
- Median Absolute Deviation: 0.18
- 1% percentile: -1.0998111724853517
- 99% percentile: 1.262142181396477
GPC1 g (aperture) - SDSS g (fiberMag):
- Median: -0.56
- Median Absolute Deviation: 0.26
- 1% percentile: -2.496896743774414
- 99% percentile: 1.7368240356445312
GPC1 g (total) - SDSS g (petroMag):
- Median: -0.56
- Median Absolute Deviation: 0.26
- 1% percentile: -2.496896743774414
- 99% percentile: 1.7368240356445312
WFC r (aperture) - SDSS r (fiberMag):
- Median: -0.35
- Median Absolute Deviation: 0.07
- 1% percentile: -0.9245659255981445
- 99% percentile: 0.3214846229553226
WFC r (total) - SDSS r (petroMag):
- Median: -0.35
- Median Absolute Deviation: 0.07
- 1% percentile: -0.9245659255981445
- 99% percentile: 0.3214846229553226
No sources have both SDSS r (fiberMag) and RCS r (aperture) values.
No sources have both SDSS r (petroMag) and RCS r (total) values.
CFHT Megacam r (aperture) - SDSS r (fiberMag):
- Median: -0.21
- Median Absolute Deviation: 0.14
- 1% percentile: -0.8575413894653321
- 99% percentile: 2.322139625549326
CFHT Megacam r (total) - SDSS r (petroMag):
- Median: -0.21
- Median Absolute Deviation: 0.14
- 1% percentile: -0.8575413894653321
- 99% percentile: 2.322139625549326
GPC1 r (aperture) - SDSS r (fiberMag):
- Median: -0.43
- Median Absolute Deviation: 0.14
- 1% percentile: -1.5779302787780762
- 99% percentile: 0.6094411659240673
GPC1 r (total) - SDSS r (petroMag):
- Median: -0.43
- Median Absolute Deviation: 0.14
- 1% percentile: -1.5779302787780762
- 99% percentile: 0.6094411659240673
WFC i (aperture) - SDSS i (fiberMag):
- Median: -0.41
- Median Absolute Deviation: 0.08
- 1% percentile: -0.9962218856811523
- 99% percentile: 0.35475969314575084
WFC i (total) - SDSS i (petroMag):
- Median: -0.41
- Median Absolute Deviation: 0.08
- 1% percentile: -0.9962218856811523
- 99% percentile: 0.35475969314575084
No sources have both SDSS i (fiberMag) and RCS i (aperture) values.
No sources have both SDSS i (petroMag) and RCS i (total) values.
GPC1 i (aperture) - SDSS i (fiberMag):
- Median: -0.41
- Median Absolute Deviation: 0.12
- 1% percentile: -1.1483933067321777
- 99% percentile: 0.3729721450805654
GPC1 i (total) - SDSS i (petroMag):
- Median: -0.41
- Median Absolute Deviation: 0.12
- 1% percentile: -1.1483933067321777
- 99% percentile: 0.3729721450805654
WFC z (aperture) - SDSS z (fiberMag):
- Median: -0.35
- Median Absolute Deviation: 0.12
- 1% percentile: -1.2592236328125
- 99% percentile: 0.6335509490966799
WFC z (total) - SDSS z (petroMag):
- Median: -0.35
- Median Absolute Deviation: 0.12
- 1% percentile: -1.2592236328125
- 99% percentile: 0.6335509490966799
No sources have both SDSS z (fiberMag) and RCS z (aperture) values.
No sources have both SDSS z (petroMag) and RCS z (total) values.
CFHT Megacam z (aperture) - SDSS z (fiberMag):
- Median: -0.14
- Median Absolute Deviation: 0.20
- 1% percentile: -1.1127138900756837
- 99% percentile: 2.7650779533386163
CFHT Megacam z (total) - SDSS z (petroMag):
- Median: -0.14
- Median Absolute Deviation: 0.20
- 1% percentile: -1.1127138900756837
- 99% percentile: 2.7650779533386163
GPC1 z (aperture) - SDSS z (fiberMag):
- Median: -0.32
- Median Absolute Deviation: 0.17
- 1% percentile: -1.4175505828857422
- 99% percentile: 1.0491403579711862
GPC1 z (total) - SDSS z (petroMag):
- Median: -0.32
- Median Absolute Deviation: 0.17
- 1% percentile: -1.4175505828857422
- 99% percentile: 1.0491403579711862

III.b - Comparing J and K bands to 2MASS

The catalogue is cross-matched to 2MASS-PSC withing 0.2 arcsecond. We compare the UKIDSS total J and K magnitudes to those from 2MASS.

The 2MASS magnitudes are “Vega-like” and we have to convert them to AB magnitudes using the zero points provided on this page:

Band Fν - 0 mag (Jy)
J 1594
H 1024
Ks 666.7

In addition, UKIDSS uses a K band whereas 2MASS uses a Ks (“short”) band, this page give a correction to convert the K band in a Ks band with the formula:

$$K_{s(2MASS)} = K_{UKIRT} + 0.003 + 0.004 * (J−K)_{UKIRT}$$
In [13]:
# The AB zero point is 3631 Jy
j_2mass_to_ab = 2.5 * np.log10(3631/1595)
k_2mass_to_ab = 2.5 * np.log10(3631/666.7)
In [14]:
twomass = Table.read("../../dmu0/dmu0_2MASS-point-sources/data/2MASS-PSC_Lockman-SWIRE.fits")
twomass_coords = SkyCoord(twomass['raj2000'], twomass['dej2000'])

idx, d2d, _ = twomass_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

twomass = twomass[mask]
ml_twomass_idx = idx[mask]
In [15]:
nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_wfcam_j'][ml_twomass_idx],
                labels=("2MASS J", "WFCAM J (total)"))
WFCAM J (total) - 2MASS J:
- Median: 0.02
- Median Absolute Deviation: 0.05
- 1% percentile: -0.9130657836820437
- 99% percentile: 0.575751805124098
In [16]:
wfcam_ks_like = master_catalogue['m_wfcam_k'] + 0.003 + 0.004 * (
    master_catalogue['m_wfcam_j'] - master_catalogue['m_wfcam_k'])
nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, wfcam_ks_like[ml_twomass_idx],
                labels=("2MASS Ks", "WFCAM Ks-like (total)"))
WFCAM Ks-like (total) - 2MASS Ks:
- Median: 0.07
- Median Absolute Deviation: 0.08
- 1% percentile: -0.8240238957842398
- 99% percentile: 0.9100533730451055

Keeping only sources with good signal to noise ratio

From here, we are only comparing sources with a signal to noise ratio above 3, i.e. roughly we a magnitude error below 0.3.

To make it easier, we are setting to NaN in the catalogue the magnitudes associated with an error above 0.3 so we can't use these magnitudes after the next cell.

In [17]:
for error_column in [_ for _ in master_catalogue.colnames if _.startswith('merr_')]:
    column = error_column.replace("merr", "m")
    keep_mask = np.isfinite(master_catalogue[error_column])
    keep_mask[keep_mask] &= master_catalogue[keep_mask][error_column] <= 0.3
    master_catalogue[column][~keep_mask] = np.nan

IV - Comparing aperture magnitudes to total ones.

In [18]:
nb_ccplots(
    master_catalogue['m_cfht_megacam_r'],
    master_catalogue['m_ap_cfht_megacam_r'] - master_catalogue['m_cfht_megacam_r'],
    "r total magnitude (CFHT)", "r aperture mag - total mag (CFHT)",
    master_catalogue["stellarity"],
    invert_x=True
)
Number of source used: 2456857 / 4366332 (56.27%)

V - Color-color and magnitude-color plots

In [19]:
nb_ccplots(
    master_catalogue['m_wfc_g'] - master_catalogue['m_wfc_i'],
    master_catalogue['m_wfcam_j'] - master_catalogue['m_wfcam_k'],
    "g - i (WFC)", "J - K (UKIDSS)",
    master_catalogue["stellarity"]
)
Number of source used: 85805 / 4366332 (1.97%)
In [20]:
nb_ccplots(
    master_catalogue['m_wfc_i'] - master_catalogue['m_irac_i1'],
    master_catalogue['m_wfc_g'] - master_catalogue['m_wfc_i'],
    "WFC i - IRAC1", "g - i (WFC)",
    master_catalogue["stellarity"]
)
Number of source used: 129379 / 4366332 (2.96%)
In [21]:
nb_ccplots(
    master_catalogue['m_cfht_megacam_u'] - master_catalogue['m_cfht_megacam_g'],
    master_catalogue['m_cfht_megacam_g'] - master_catalogue['m_cfht_megacam_r'],
    "u - g (CFHT)", "g - r (CFHT)",
    master_catalogue["stellarity"]
)
Number of source used: 2070789 / 4366332 (47.43%)
In [22]:
nb_ccplots(
    master_catalogue['m_wfcam_j'] - master_catalogue['m_wfcam_k'],
    master_catalogue['m_wfc_g'] - master_catalogue['m_wfcam_j'],
    "J - K (WFCAM)", "g - J (WFC, WFCAM)",
    master_catalogue["stellarity"]
)
Number of source used: 108739 / 4366332 (2.49%)
In [23]:
nb_ccplots(
    master_catalogue['m_wfc_i'] - master_catalogue['m_wfc_z'],
    master_catalogue['m_wfc_z'] - master_catalogue['m_wfcam_j'],
    "i - z (WFC)", "z - J (WFC, WFCAM)",
    master_catalogue["stellarity"]
)
Number of source used: 11038 / 4366332 (0.25%)
In [24]:
nb_ccplots(
    master_catalogue['m_irac_i3'] - master_catalogue['m_irac_i4'],
    master_catalogue['m_irac_i1'] - master_catalogue['m_irac_i2'],
    "IRAC3 - IRAC4", "IRAC1 - IRAC2",
    master_catalogue["stellarity"]
)
Number of source used: 38805 / 4366332 (0.89%)